Stochastic Graph Transformation For Social Network Modeling

Nicolas Behr
(Univ. de Paris, CNRS, IRIF Paris, France)
Bello Shehu Bello
(Dept. of Computer Science Bayero Univ. Kano, Nigeria)
Sebastian Ehmes
(Real-Time Systems Lab TU Darmstadt, Germany)
Reiko Heckel
(School of Informatics Univ. of Leicester, UK)

Adaptive networks model social, physical, technical, or biological systems as attributed graphs evolving at the level of both their topology and data. They are naturally described by graph transformation, but the majority of authors take an approach inspired by the physical sciences, combining an informal description of the operations with programmed simulations, and systems of ODEs as the only abstract mathematical description. We show that we can capture a range of social network models, the so-called voter models, as stochastic attributed graph transformation systems, demonstrate the benefits of this representation and establish its relation to the non-standard probabilistic view adopted in the literature. We use the theory and tools of graph transformation to analyze and simulate the models and propose a new variant of a standard stochastic simulation algorithm to recreate the results observed.

In Berthold Hoffmann and Mark Minas: Proceedings Twelfth International Workshop on Graph Computational Models (GCM 2021), Online, 22nd June 2021, Electronic Proceedings in Theoretical Computer Science 350, pp. 35–50.
Published: 21st December 2021.

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